Towards a Motivated Annotation Schema of Collocation Errors in Learner Corpora

Abstract

Collocations play a significant role in second language acquisition.In order to be able to offer efficient support to learners, an NLP-based CALL environment for learning collocations should be based on a representative collocation error annotated learner corpus.However, so far, no theoretically-motivated collocation error tag set is available.Existing learner corpora tag collocation errors simply as "lexical errors" -which is clearly insufficient given the wide range of different collocation errors that the learners make.In this paper, we present a fine-grained three-dimensional typology of collocation errors that has been derived in an empirical study from the learner corpus CEDEL2 compiled by a team at the Autonomous University of Madrid.The first dimension captures whether the error concerns the collocation as a whole or one of its elements; the second dimension captures the language-oriented error analysis, while the third exemplifies the interpretative error analysis.To facilitate a smooth annotation along this typology, we adapted Knowtator, a flexible off-the-shelf annotation tool implemented as a Protégé plugin.

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